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from torch.autograd import Variable | ||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
import numpy as np | ||
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class SerializableModule(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
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def save(self, filename): | ||
torch.save(self.state_dict(), filename) | ||
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def load(self, filename): | ||
self.load_state_dict(torch.load(filename, map_location=lambda storage, loc: storage)) | ||
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class VDPWIConvNet(SerializableModule): | ||
def __init__(self, n_labels): | ||
self.conv1 = nn.Conv2d(13, 128, 3, padding=1) | ||
self.conv2 = nn.Conv2d(128, 164, 3, padding=1) | ||
self.conv3 = nn.Conv2d(164, 192, 3, padding=1) | ||
self.conv4 = nn.Conv2d(192, 192, 3, padding=1) | ||
self.conv5 = nn.Conv2d(192, 128, 3, padding=1) | ||
self.maxpool2 = nn.MaxPool2d(2, ceil_mode=True) | ||
self.dnn = nn.Linear(128, 128) | ||
self.output = nn.Linear(128, n_labels) | ||
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def forward(self, x): | ||
pool_final = nn.MaxPool2d(2, ceil_mode=True) if x.size(2) == 32 else nn.MaxPool2d(3, 1, ceil_mode=True) | ||
x = self.maxpool2(F.relu(self.conv1(x))) | ||
x = self.maxpool2(F.relu(self.conv2(x))) | ||
x = self.maxpool2(F.relu(self.conv3(x))) | ||
x = self.maxpool2(F.relu(self.conv4(x))) | ||
x = pool_final(F.relu(self.conv5(x))) | ||
x = F.relu(self.dnn(x.view(x.size(0), -1))) | ||
return self.output(x) | ||
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class VDPWIModel(SerializableModule): | ||
def __init__(self, embedding, config, classifier_net=None): | ||
super().__init__() | ||
self.rnn = nn.LSTM(300, config.rnn_hidden_dim, 1, bidirectional=True) | ||
self.embedding = embedding | ||
self.classifier_net = VDPWIConvNet(config.n_labels) if classifier is None else classifier_net | ||
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def compute_sim_cube(self, seq1, seq2): | ||
def compute_sim(h1, h2): | ||
h1_len = torch.sqrt(torch.sum(h1**2)) | ||
h2_len = torch.sqrt(torch.sum(h2**2)) | ||
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dot_prod = torch.dot(h1, h2) | ||
cos_dist = dot_prod / (h1_len * h2_len + 1E-8) | ||
l2_dist = torch.sqrt(torch.sum((h1 - h2)**2)) | ||
return dot_prod, cos_dist, l2_dist | ||
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sim_cube = Variable(torch.Tensor(13, seq1.size(0), seq2.size(0)).cuda()) | ||
seq1_f = seq1[:, 0] | ||
seq1_b = seq1[:, 1] | ||
seq2_f = seq2[:, 0] | ||
seq2_b = seq2[:, 1] | ||
for t, (h1f, h1b) in enumerate(zip(seq1_f, seq1_b)): | ||
for s, (h2f, h2b) in enumerate(zip(seq2_f, seq2_b)): | ||
sim_cube[0:3, t, s] = compute_sim(torch.cat([h1f, h1b]), torch.cat([h2f, h2b])) | ||
sim_cube[3:6, t, s] = compute_sim(h1f, h2f) | ||
sim_cube[6:9, t, s] = compute_sim(h1b, h2b) | ||
sim_cube[9:12, t, s] = compute_sim(h1f + h1b, h2f + h2b) | ||
return sim_cube | ||
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def compute_focus_cube(self, sim_cube): | ||
mask = Variable(torch.Tensor(*sim_cube.size()).cuda()) | ||
def build_mask(index): | ||
s1tag = np.zeros(sim_cube.size(1)) | ||
s2tag = np.zeros(sim_cube.size(2)) | ||
_, indices = torch.sort(sim_cube[index].view(-1), descending=True) | ||
for i, index in enumerate(indices): | ||
if i >= len(s1tag) + len(s2tag): | ||
break | ||
pos1, pos2 = index // len(s1tag), index % len(s2tag) | ||
if s1tag[pos1] + s2tag[pos2] == 0: | ||
s1tag[pos1] = s2tag[pos2] = 1 | ||
mask[:, pos1, pos2] = 1 | ||
build_mask(10) | ||
build_mask(11) | ||
mask[12, :, :] = 1 | ||
return mask * sim_cube | ||
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def forward(self, x1, x2): | ||
x1 = self.embedding(x1) | ||
x2 = self.embedding(x2) | ||
seq1, _ = self.rnn(x1, batch_first=True) | ||
seq2, _ = self.rnn(x2, batch_first=True) | ||
seq1 = seq1.squeeze(1) # batch size assumed to be 1 | ||
seq2 = seq2.squeeze(1) | ||
sim_cube = self.compute_sim_cube(seq1, seq2) | ||
focus_cube = self.compute_focus_cube(sim_cube) | ||
logits = self.classifier_net(focus_cube.unsqueeze(0)) | ||
return torch.log(F.softmax(logits)) |